{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data sources of 2019 nCoV\n",
    "Acknowledgement: \n",
    "- API from https://lab.isaaclin.cn/nCoV/\n",
    "- https://github.com/jianxu305/nCov2019_analysis/blob/master/src/demo.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实时获取需要的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_time(data):\n",
    "    df = pd.DataFrame(data)\n",
    "    try:\n",
    "        if np.any(['Time' in name for name in df.columns]):\n",
    "            for time_name in df.columns[['Time' in name for name in df.columns]]:\n",
    "                df[time_name] = pd.to_datetime(df.loc[:, time_name], unit='ms')\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "    # add new column of the updating \"Date\" instead of concrete time\n",
    "    if 'updateTime' in df.columns:\n",
    "        df['updateDate'] = pd.Series([pd.to_datetime(item).date() for item in df['updateTime']])\n",
    "    elif 'pubDate' in df.columns:\n",
    "        df['pubTime'] = pd.to_datetime(df.loc[:, 'pubDate'], unit='ms')\n",
    "        df['pubDate'] = pd.Series([pd.to_datetime(item).date() for item in df['pubTime']])\n",
    "        \n",
    "    return df\n",
    "\n",
    "def query_counts_data(category='area', archival=False, province='all'):\n",
    "    '''\n",
    "    API for retrieving data from https://lab.isaaclin.cn/nCoV/.\n",
    "    \n",
    "    Parameters:\n",
    "        Category (str): available options are 'overall', 'area'.\n",
    "            Check the above website for more.\n",
    "        archival (bool): whether retrieve archival time-series data. \n",
    "            Default is False, only retrieve today's data.\n",
    "        province (str): name of specific province. Use 'all' to get data from all provinces and countries.\n",
    "            Notice: full name is required (\"湖北省\", instead of \"湖北\"). \n",
    "    Returns:\n",
    "        df (pandas.DataFrame): dataframe object. \n",
    "    \n",
    "    '''\n",
    "    import requests\n",
    "    import pandas as pd\n",
    "    assert isinstance(category, str), 'Input \"catecory\" must be a string!'\n",
    "    \n",
    "    url = 'https://lab.isaaclin.cn/nCoV/api/' + category\n",
    "    url += '?latest={}'.format(int(not archival))\n",
    "    if province is not 'all':\n",
    "        url += '&province=' + province\n",
    "\n",
    "    req = requests.get(url)\n",
    "    if req.status_code != 200 or req.json()['success'] is False:\n",
    "        raise ValueError('The connection fails! Please check input arguments.')\n",
    "        return False\n",
    "    else:\n",
    "        results = req.json()['results']\n",
    "        df = parse_time(results)\n",
    "        return df\n",
    "    \n",
    "def aggregate_Daily(df):\n",
    "    frm_list = []\n",
    "    for key, frm in df.sort_values(['updateDate']).groupby(['provinceName', 'updateDate']):\n",
    "        frm_list.append(frm.sort_values(['updateTime'])[-1:])\n",
    "    return pd.concat(frm_list).sort_values(['updateTime', 'provinceName']).loc[::-1]\n",
    "\n",
    "def parse_city(city_row):\n",
    "    return pd.DataFrame(city_row.values[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>provinceName</th>\n",
       "      <th>provinceShortName</th>\n",
       "      <th>confirmedCount</th>\n",
       "      <th>suspectedCount</th>\n",
       "      <th>curedCount</th>\n",
       "      <th>deadCount</th>\n",
       "      <th>comment</th>\n",
       "      <th>locationId</th>\n",
       "      <th>cities</th>\n",
       "      <th>country</th>\n",
       "      <th>updateTime</th>\n",
       "      <th>createTime</th>\n",
       "      <th>modifyTime</th>\n",
       "      <th>operator</th>\n",
       "      <th>updateDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>湖北省</td>\n",
       "      <td>湖北</td>\n",
       "      <td>24953</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1204</td>\n",
       "      <td>699</td>\n",
       "      <td></td>\n",
       "      <td>420000.0</td>\n",
       "      <td>[{'cityName': '武汉', 'confirmedCount': 13603, '...</td>\n",
       "      <td>中国</td>\n",
       "      <td>2020-02-08 09:10:41.955</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-02-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>湖北省</td>\n",
       "      <td>湖北</td>\n",
       "      <td>24953</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1133</td>\n",
       "      <td>699</td>\n",
       "      <td></td>\n",
       "      <td>420000.0</td>\n",
       "      <td>[{'cityName': '武汉', 'confirmedCount': 13603, '...</td>\n",
       "      <td>中国</td>\n",
       "      <td>2020-02-08 08:19:57.683</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-02-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>湖北省</td>\n",
       "      <td>湖北</td>\n",
       "      <td>24953</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1132</td>\n",
       "      <td>699</td>\n",
       "      <td></td>\n",
       "      <td>420000.0</td>\n",
       "      <td>[{'cityName': '武汉', 'confirmedCount': 13603, '...</td>\n",
       "      <td>中国</td>\n",
       "      <td>2020-02-08 07:49:31.333</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-02-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  provinceName provinceShortName  confirmedCount  suspectedCount  curedCount  \\\n",
       "0          湖北省                湖北           24953             0.0        1204   \n",
       "1          湖北省                湖北           24953             0.0        1133   \n",
       "2          湖北省                湖北           24953             0.0        1132   \n",
       "\n",
       "   deadCount comment  locationId  \\\n",
       "0        699            420000.0   \n",
       "1        699            420000.0   \n",
       "2        699            420000.0   \n",
       "\n",
       "                                              cities country  \\\n",
       "0  [{'cityName': '武汉', 'confirmedCount': 13603, '...      中国   \n",
       "1  [{'cityName': '武汉', 'confirmedCount': 13603, '...      中国   \n",
       "2  [{'cityName': '武汉', 'confirmedCount': 13603, '...      中国   \n",
       "\n",
       "               updateTime createTime modifyTime operator  updateDate  \n",
       "0 2020-02-08 09:10:41.955        NaT        NaT      NaN  2020-02-08  \n",
       "1 2020-02-08 08:19:57.683        NaT        NaT      NaN  2020-02-08  \n",
       "2 2020-02-08 07:49:31.333        NaT        NaT      NaN  2020-02-08  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area = query_counts_data(category='area', archival=True, province='湖北省') # not very slow\n",
    "area[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = aggregate_Daily(area)[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cityName</th>\n",
       "      <th>confirmedCount</th>\n",
       "      <th>suspectedCount</th>\n",
       "      <th>curedCount</th>\n",
       "      <th>deadCount</th>\n",
       "      <th>locationId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>武汉</td>\n",
       "      <td>10117</td>\n",
       "      <td>0</td>\n",
       "      <td>432</td>\n",
       "      <td>414</td>\n",
       "      <td>420100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>孝感</td>\n",
       "      <td>1886</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>420900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>黄冈</td>\n",
       "      <td>1807</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>29</td>\n",
       "      <td>421100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>随州</td>\n",
       "      <td>834</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>421300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>荆州</td>\n",
       "      <td>801</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>421000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>襄阳</td>\n",
       "      <td>787</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>420600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>黄石</td>\n",
       "      <td>566</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>420200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>宜昌</td>\n",
       "      <td>563</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>420500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>荆门</td>\n",
       "      <td>508</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>420800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>鄂州</td>\n",
       "      <td>423</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>420700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>咸宁</td>\n",
       "      <td>399</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>421200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>十堰</td>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>420300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>仙桃</td>\n",
       "      <td>265</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>429004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>恩施州</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>422800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>天门</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>429006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>潜江</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>429005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>神农架林区</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>429021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cityName  confirmedCount  suspectedCount  curedCount  deadCount  locationId\n",
       "0        武汉           10117               0         432        414      420100\n",
       "1        孝感            1886               0           9         25      420900\n",
       "2        黄冈            1807               0          62         29      421100\n",
       "3        随州             834               0           9          9      421300\n",
       "4        荆州             801               0          18         10      421000\n",
       "5        襄阳             787               0          10          2      420600\n",
       "6        黄石             566               0          25          2      420200\n",
       "7        宜昌             563               0           9          6      420500\n",
       "8        荆门             508               0          21         17      420800\n",
       "9        鄂州             423               0           8         18      420700\n",
       "10       咸宁             399               0           3          1      421200\n",
       "11       十堰             353               0          14          0      420300\n",
       "12       仙桃             265               0           0          5      429004\n",
       "13      恩施州             144               0          10          0      422800\n",
       "14       天门             138               0           1         10      429006\n",
       "15       潜江              64               0           0          1      429005\n",
       "16    神农架林区              10               0           2          0      429021"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df['updateDate'] == datetime.date(year=2020, month=2, day=5)\n",
    "city = parse_city(df[mask]['cities'])\n",
    "city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.size'] = 12.0\n",
    "\n",
    "fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(13, 10))\n",
    "df.plot(y=['confirmedCount'], x='updateDate', style='-*', ax=ax1, grid=True, logy=False, color='black', marker='o')\n",
    "ax1.set_ylabel(\"Confirmed\")\n",
    "\n",
    "df.plot(y=['deadCount', 'curedCount'], x='updateDate', style='-*', grid=True, ax=ax2, sharex=True)\n",
    "ax2.set_ylabel(\"Counts\")\n",
    "\n",
    "plt.subplots_adjust(hspace=0.0)\n",
    "ax1.tick_params(direction='in')\n",
    "ax2.tick_params(direction='in')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 利用已有的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "non convertible value 2020-02-08 16:35:10.822 with the unit 'ms'\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('https://github.com/BlankerL/DXY-2019-nCoV-Data/raw/master/csv/DXYArea.csv')\n",
    "df = parse_time(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aggregate_daily_csv(df):\n",
    "    frm_list = []\n",
    "    for key, frm in df.sort_values(['updateDate']).groupby(['provinceName', 'cityName', 'updateDate']):\n",
    "        frm_list.append(frm.sort_values(['updateTime'])[-1:])\n",
    "    return pd.concat(frm_list).sort_values(['updateTime', 'provinceName', 'cityName']).loc[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "jingmen = df[df['cityName'] == '荆门']\n",
    "jingmen_daily = aggregate_daily_csv(jingmen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9839231fd0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jingmen_daily.plot(y='city_confirmedCount', x='updateDate', style='-*', figsize=(10, 6), title='Jingmen')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 新闻报道？？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_news_data(category='news', num='all', province='all'):\n",
    "    '''\n",
    "    API for retrieving news data from https://lab.isaaclin.cn/nCoV/.\n",
    "    \n",
    "    Parameters:\n",
    "        Category (str): available options are 'news'.\n",
    "            Check the above website for more.\n",
    "        archival (bool): whether retrieve archival time-series data. \n",
    "            Default is False, only retrieve today's data.\n",
    "        province (str): name of specific province. Use 'all' to get data from all provinces and countries.\n",
    "            Notice: full name is required (\"湖北省\", instead of \"湖北\"). \n",
    "    Returns:\n",
    "        df (pandas.DataFrame): dataframe object. \n",
    "    \n",
    "    '''\n",
    "    import requests\n",
    "    import pandas as pd\n",
    "    assert isinstance(category, str), 'Input \"catecory\" must be a string!'\n",
    "    \n",
    "    url = 'https://lab.isaaclin.cn/nCoV/api/' + category\n",
    "    url += '?num={}'.format(num)\n",
    "    if province is not 'all':\n",
    "        url += '&province=' + province\n",
    "    \n",
    "    req = requests.get(url)\n",
    "    if req.status_code != 200 or req.json()['success'] is False:\n",
    "        raise ValueError('The connection fails! Please check input arguments.')\n",
    "        return False\n",
    "    else:\n",
    "        results = req.json()['results']\n",
    "        df = parse_time(results)\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "news = query_news_data(category='news', num='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>summary</th>\n",
       "      <th>infoSource</th>\n",
       "      <th>sourceUrl</th>\n",
       "      <th>pubDate</th>\n",
       "      <th>provinceId</th>\n",
       "      <th>pubTime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>provinceName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>湖北省</th>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>全国</th>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京市</th>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广东省</th>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津市</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海市</th>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山东省</th>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>辽宁省</th>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川省</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>浙江省</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>福建省</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南省</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重庆市</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>安徽省</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黑龙江省</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江西省</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖南省</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河南省</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江苏省</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广西壮族自治区</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海南省</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贵州省</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙古自治区</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林省</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>陕西省</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河北省</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>青海省</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>宁夏回族自治区</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山西省</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆维吾尔自治区</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>甘肃省</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西藏自治区</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>香港</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳门</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>台湾</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              title  summary  infoSource  sourceUrl  pubDate  provinceId  \\\n",
       "provinceName                                                               \n",
       "湖北省             163      163         163        163      163         163   \n",
       "全国              113      113         113        113      113         113   \n",
       "北京市              82       82          82         82       82          82   \n",
       "广东省              58       58          58         58       58          58   \n",
       "天津市              49       49          49         49       49          49   \n",
       "上海市              46       46          46         46       46          46   \n",
       "山东省              44       44          44         44       44          44   \n",
       "辽宁省              37       37          37         37       37          37   \n",
       "四川省              32       32          32         32       32          32   \n",
       "浙江省              30       30          30         30       30          30   \n",
       "福建省              30       30          30         30       30          30   \n",
       "云南省              29       29          29         29       29          29   \n",
       "重庆市              29       29          29         29       29          29   \n",
       "安徽省              28       28          28         28       28          28   \n",
       "黑龙江省             27       27          27         27       27          27   \n",
       "江西省              27       27          27         27       27          27   \n",
       "湖南省              26       26          26         26       26          26   \n",
       "河南省              25       25          25         25       25          25   \n",
       "江苏省              23       23          23         23       23          23   \n",
       "广西壮族自治区          23       23          23         23       23          23   \n",
       "海南省              23       23          23         23       23          23   \n",
       "贵州省              23       23          23         23       23          23   \n",
       "内蒙古自治区           22       22          22         22       22          22   \n",
       "吉林省              20       20          20         20       20          20   \n",
       "陕西省              20       20          20         20       20          20   \n",
       "河北省              20       20          20         20       20          20   \n",
       "青海省              16       16          16         16       16          16   \n",
       "宁夏回族自治区          16       16          16         16       16          16   \n",
       "山西省              16       16          16         16       16          16   \n",
       "新疆维吾尔自治区         15       15          15         15       15          15   \n",
       "甘肃省              13       13          13         13       13          13   \n",
       "西藏自治区            10       10          10         10       10          10   \n",
       "香港               10       10          10         10       10          10   \n",
       "澳门                6        6           6          6        6           6   \n",
       "台湾                3        3           3          3        3           3   \n",
       "\n",
       "              pubTime  \n",
       "provinceName           \n",
       "湖北省               163  \n",
       "全国                113  \n",
       "北京市                82  \n",
       "广东省                58  \n",
       "天津市                49  \n",
       "上海市                46  \n",
       "山东省                44  \n",
       "辽宁省                37  \n",
       "四川省                32  \n",
       "浙江省                30  \n",
       "福建省                30  \n",
       "云南省                29  \n",
       "重庆市                29  \n",
       "安徽省                28  \n",
       "黑龙江省               27  \n",
       "江西省                27  \n",
       "湖南省                26  \n",
       "河南省                25  \n",
       "江苏省                23  \n",
       "广西壮族自治区            23  \n",
       "海南省                23  \n",
       "贵州省                23  \n",
       "内蒙古自治区             22  \n",
       "吉林省                20  \n",
       "陕西省                20  \n",
       "河北省                20  \n",
       "青海省                16  \n",
       "宁夏回族自治区            16  \n",
       "山西省                16  \n",
       "新疆维吾尔自治区           15  \n",
       "甘肃省                13  \n",
       "西藏自治区              10  \n",
       "香港                 10  \n",
       "澳门                  6  \n",
       "台湾                  3  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news.groupby('provinceName').count().sort_values('title')[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>summary</th>\n",
       "      <th>infoSource</th>\n",
       "      <th>sourceUrl</th>\n",
       "      <th>pubDate</th>\n",
       "      <th>provinceName</th>\n",
       "      <th>provinceId</th>\n",
       "      <th>pubTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>广东新增确诊20例，累计确诊1095例</td>\n",
       "      <td>截至2月8日12时，全省累计报告新型冠状病毒感染的肺炎确诊病例1095例。8日当天0-12时...</td>\n",
       "      <td>广东卫健委</td>\n",
       "      <td>http://wsjkw.gd.gov.cn/xxgzbdfk/yqtb/content/p...</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>广东省</td>\n",
       "      <td>44</td>\n",
       "      <td>2020-02-08 08:05:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>国家邮政局：快递2月中旬恢复到4成以上</td>\n",
       "      <td>7日，国家邮政局召开部分快递企业专题电话会议指出，分阶段确定快递企业恢复生产目标，积极推进网...</td>\n",
       "      <td>人民网</td>\n",
       "      <td>http://m.weibo.cn/2286908003/4469721326007093</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>全国</td>\n",
       "      <td>100</td>\n",
       "      <td>2020-02-08 07:47:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>新冠肺炎传播途径含气溶胶传播</td>\n",
       "      <td>今天，上海疫情防控工作发布会介绍：卫生防疫专家强调，目前可以确定的新冠肺炎传播途径主要为直接...</td>\n",
       "      <td>央视新闻</td>\n",
       "      <td>http://m.weibo.cn/2656274875/4469720700817749</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>全国</td>\n",
       "      <td>100</td>\n",
       "      <td>2020-02-08 07:45:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>今天湖北企业自产N95口罩超12万，预计下周可自产自足</td>\n",
       "      <td>从湖北省新冠肺炎疫情防控指挥部了解到，今天省内企业自产N95口罩超过12万只，预计下周可实现...</td>\n",
       "      <td>人民日报</td>\n",
       "      <td>http://m.weibo.cn/2803301701/4469714216219398</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>湖北省</td>\n",
       "      <td>42</td>\n",
       "      <td>2020-02-08 07:19:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>安徽新增治愈12，累计治愈59</td>\n",
       "      <td>今天，安徽有12名新冠肺炎患者治愈出院。其中，合肥2例，铜陵2例，淮北1例，宿州1例，阜阳2...</td>\n",
       "      <td>央视新闻</td>\n",
       "      <td>http://m.weibo.cn/2656274875/4469713495107536</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>安徽省</td>\n",
       "      <td>34</td>\n",
       "      <td>2020-02-08 07:16:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>国家卫健委：新冠肺炎英文简称NCP</td>\n",
       "      <td>新闻发言人介绍新型冠状病毒感染的肺炎统一称谓为“新型冠状病毒肺炎”，简称“新冠肺炎”，英文简...</td>\n",
       "      <td>人民日报</td>\n",
       "      <td>http://m.weibo.cn/2803301701/4469712253795751</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>全国</td>\n",
       "      <td>100</td>\n",
       "      <td>2020-02-08 07:11:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>辽宁新增确诊4例，累计确诊103例</td>\n",
       "      <td>2020年2月7日22时至2月8日13时，辽宁省新增4例新型冠状病毒感染的肺炎确诊病例,其中...</td>\n",
       "      <td>辽宁卫健委</td>\n",
       "      <td>http://wsjk.ln.gov.cn/wst_zdzt/xxgzbd/yqtb/202...</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>辽宁省</td>\n",
       "      <td>21</td>\n",
       "      <td>2020-02-08 06:41:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>上海新增治愈11，累计治愈41</td>\n",
       "      <td>上海市卫健委今天通报：11例新冠肺炎病例，经定点医疗机构医护人员精心诊治和护理，专家组评估，...</td>\n",
       "      <td>央视新闻</td>\n",
       "      <td>http://m.weibo.cn/2656274875/4469685842067584</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>上海市</td>\n",
       "      <td>31</td>\n",
       "      <td>2020-02-08 05:26:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>福建新增15例，累计239例</td>\n",
       "      <td>2月7日0-24时，福建省报告新增新型冠状病毒肺炎确诊病例15例。报告新增新型冠状病毒肺炎疑...</td>\n",
       "      <td>人民网</td>\n",
       "      <td>http://m.weibo.cn/2286908003/4469652887505637</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>福建省</td>\n",
       "      <td>35</td>\n",
       "      <td>2020-02-08 03:15:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>内蒙古新增确诊病例2例，累计52例</td>\n",
       "      <td>2020年2月7日9时至8日10时，内蒙古自治区报告新型冠状病毒感染的肺炎新增确诊病例2例。...</td>\n",
       "      <td>内蒙古卫健委</td>\n",
       "      <td>http://wjw.nmg.gov.cn/doc/2020/02/08/291119.shtml</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>内蒙古自治区</td>\n",
       "      <td>15</td>\n",
       "      <td>2020-02-08 03:00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>北京新增18例，累计315例</td>\n",
       "      <td>2月7日0时至24时，北京市新增18例新型冠状病毒感染的肺炎病例。截至2月7日24时，北京市...</td>\n",
       "      <td>人民网</td>\n",
       "      <td>http://m.weibo.cn/2286908003/4469647812542639</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>北京市</td>\n",
       "      <td>11</td>\n",
       "      <td>2020-02-08 02:55:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>贵州新增8例，累计89例</td>\n",
       "      <td>2020年2月7日12—24时，全省新型冠状病毒感染的肺炎新增确诊病例8例,新增治愈出院病例...</td>\n",
       "      <td>人民网</td>\n",
       "      <td>http://m.weibo.cn/2286908003/4469633911975612</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>贵州省</td>\n",
       "      <td>52</td>\n",
       "      <td>2020-02-08 02:00:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>湖北以外地区新增病例连降4天</td>\n",
       "      <td>据国家卫健委数据统计，2月7日0—24时，全国湖北以外地区新增确诊病例558例，连续第4日呈...</td>\n",
       "      <td>人民网</td>\n",
       "      <td>http://m.weibo.cn/2286908003/4469630049543788</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>湖北省</td>\n",
       "      <td>42</td>\n",
       "      <td>2020-02-08 01:45:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>宁夏新增2例，累计45例</td>\n",
       "      <td>2月8日，宁夏应对新型冠状病毒感染肺炎疫情工作指挥部发布最新全区新型冠状病毒感染的肺炎疫情通...</td>\n",
       "      <td>人民网</td>\n",
       "      <td>http://m.weibo.cn/2286908003/4469629193798369</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>宁夏回族自治区</td>\n",
       "      <td>64</td>\n",
       "      <td>2020-02-08 01:41:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>广东新增57例，累计确诊1075例</td>\n",
       "      <td>截至2月7日24时，广东省累计报告新型冠状病毒感染的肺炎确诊病例1075例。2月7日当天全省...</td>\n",
       "      <td>央视新闻</td>\n",
       "      <td>http://m.weibo.cn/2656274875/4469622834888584</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>广东省</td>\n",
       "      <td>44</td>\n",
       "      <td>2020-02-08 01:16:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2月7日青海无新增病例，青海累计确诊18例</td>\n",
       "      <td>2020年2月7日0-24时，青海省报告新型冠状病毒感染的肺炎新增确诊病例0例，新增重症病例...</td>\n",
       "      <td>人民网</td>\n",
       "      <td>http://m.weibo.cn/2286908003/4469622763441202</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>青海省</td>\n",
       "      <td>63</td>\n",
       "      <td>2020-02-08 01:16:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>江西新增37例，累计确诊698例</td>\n",
       "      <td>2020年2月7日0-24时，江西省报告新型冠状病毒感染的肺炎新增确诊病例37例，新增治愈出...</td>\n",
       "      <td>央视新闻</td>\n",
       "      <td>http://m.weibo.cn/2656274875/4469619902951551</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>江西省</td>\n",
       "      <td>36</td>\n",
       "      <td>2020-02-08 01:04:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>河南新增67例，累计981例</td>\n",
       "      <td>2020年2月7日0-24时，河南省新增新型冠状病毒感染的肺炎确诊病例67例,新增出院病例2...</td>\n",
       "      <td>人民网</td>\n",
       "      <td>http://m.weibo.cn/2286908003/4469618531860175</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>河南省</td>\n",
       "      <td>41</td>\n",
       "      <td>2020-02-08 00:59:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>浙江新增42例，累计确诊1048例</td>\n",
       "      <td>2020年2月7日0-24时，浙江省报告新型冠状病毒感染的肺炎新增确诊病例42例，新增出院病...</td>\n",
       "      <td>央视新闻</td>\n",
       "      <td>http://m.weibo.cn/2656274875/4469618426626283</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>浙江省</td>\n",
       "      <td>33</td>\n",
       "      <td>2020-02-08 00:58:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>四川新增19例，累计363例</td>\n",
       "      <td>2月7日0-24时，我省新型冠状病毒肺炎新增确诊病例19例，新增治愈出院病例13例，无新增死...</td>\n",
       "      <td>人民网</td>\n",
       "      <td>http://m.weibo.cn/2286908003/4469616015191183</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>四川省</td>\n",
       "      <td>51</td>\n",
       "      <td>2020-02-08 00:49:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>湖南新增31例，累计确诊803例</td>\n",
       "      <td>2020年2月7日0-24时，湖南省报告新型冠状病毒感染的肺炎新增确诊病例31例，新增重症病...</td>\n",
       "      <td>央视新闻</td>\n",
       "      <td>http://m.weibo.cn/2656274875/4469615893625003</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>湖南省</td>\n",
       "      <td>43</td>\n",
       "      <td>2020-02-08 00:48:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>江苏新增31例，累计确诊439例</td>\n",
       "      <td>2020年2月7日0-24时，江苏省报告新型冠状病毒肺炎新增确诊病例31例。截至2月7日24...</td>\n",
       "      <td>央视新闻</td>\n",
       "      <td>http://m.weibo.cn/2656274875/4469615188708391</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>32</td>\n",
       "      <td>2020-02-08 00:46:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>黑龙江新增117例，累计234例</td>\n",
       "      <td>2020年2月7日0-24时，黑龙江省报告新型冠状病毒感染的肺炎新增疑似病例117例，无症状...</td>\n",
       "      <td>人民网</td>\n",
       "      <td>http://m.weibo.cn/2286908003/4469615009000259</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>黑龙江省</td>\n",
       "      <td>23</td>\n",
       "      <td>2020-02-08 00:45:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>安徽新增68例，累计733例</td>\n",
       "      <td>2020年2月7日0-24时，安徽省报告新增确诊病例68例，新增疑似病例70例，新增治愈出院...</td>\n",
       "      <td>人民网</td>\n",
       "      <td>http://m.weibo.cn/2286908003/4469614513948374</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>安徽省</td>\n",
       "      <td>34</td>\n",
       "      <td>2020-02-08 00:43:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>全国累计治愈出院超2千例</td>\n",
       "      <td>2月7日0—24时，31个省（自治区、直辖市）和新疆生产建设兵团报告新增治愈出院510例（湖...</td>\n",
       "      <td>央视新闻</td>\n",
       "      <td>http://m.weibo.cn/2656274875/4469610981930220</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>全国</td>\n",
       "      <td>100</td>\n",
       "      <td>2020-02-08 00:29:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>全国累计确诊新冠肺炎34546例，新增3399例</td>\n",
       "      <td>2月7日0—24时，31个省（自治区、直辖市）和新疆生产建设兵团报告，新增确诊病例3399例...</td>\n",
       "      <td>央视新闻</td>\n",
       "      <td>http://m.weibo.cn/2656274875/4469608121852346</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>全国</td>\n",
       "      <td>100</td>\n",
       "      <td>2020-02-08 00:18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>全国累计确诊新冠肺炎31774例，新增3399例新冠肺炎</td>\n",
       "      <td>2月7日0—24时，31个省（自治区、直辖市）和新疆生产建设兵团报告，新增确诊病例3399例...</td>\n",
       "      <td>央视新闻</td>\n",
       "      <td>http://m.weibo.cn/2656274875/4469607526433947</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>全国</td>\n",
       "      <td>100</td>\n",
       "      <td>2020-02-08 00:15:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>广西新增11例，累计183例</td>\n",
       "      <td>2月7日0-24时，广西共新增新型冠状病毒感染的肺炎确诊病例11例。新增疑似病例123例，现...</td>\n",
       "      <td>人民网</td>\n",
       "      <td>http://m.weibo.cn/2286908003/4469606099822822</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>广西壮族自治区</td>\n",
       "      <td>45</td>\n",
       "      <td>2020-02-08 00:09:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>吉林新增4例，累计69例</td>\n",
       "      <td>2月7日0-24时，吉林省新增确诊病例4例，由疑似病例转为确诊病例1例，在密切接触者中主动开...</td>\n",
       "      <td>人民网</td>\n",
       "      <td>http://m.weibo.cn/2286908003/4469605475029507</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>吉林省</td>\n",
       "      <td>22</td>\n",
       "      <td>2020-02-08 00:07:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>河北新增24例，累计确诊195例</td>\n",
       "      <td>2020年2月7日0—24时，河北省报告新型冠状病毒感染的肺炎新增确诊病例24例。新增治愈出...</td>\n",
       "      <td>央视新闻</td>\n",
       "      <td>http://m.weibo.cn/2656274875/4469604535935240</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>河北省</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-02-08 00:03:44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           title  \\\n",
       "0            广东新增确诊20例，累计确诊1095例   \n",
       "1            国家邮政局：快递2月中旬恢复到4成以上   \n",
       "2                 新冠肺炎传播途径含气溶胶传播   \n",
       "3    今天湖北企业自产N95口罩超12万，预计下周可自产自足   \n",
       "4                安徽新增治愈12，累计治愈59   \n",
       "5              国家卫健委：新冠肺炎英文简称NCP   \n",
       "6              辽宁新增确诊4例，累计确诊103例   \n",
       "7                上海新增治愈11，累计治愈41   \n",
       "8                 福建新增15例，累计239例   \n",
       "9              内蒙古新增确诊病例2例，累计52例   \n",
       "10                北京新增18例，累计315例   \n",
       "11                  贵州新增8例，累计89例   \n",
       "12                湖北以外地区新增病例连降4天   \n",
       "13                  宁夏新增2例，累计45例   \n",
       "14             广东新增57例，累计确诊1075例   \n",
       "15         2月7日青海无新增病例，青海累计确诊18例   \n",
       "16              江西新增37例，累计确诊698例   \n",
       "17                河南新增67例，累计981例   \n",
       "18             浙江新增42例，累计确诊1048例   \n",
       "19                四川新增19例，累计363例   \n",
       "20              湖南新增31例，累计确诊803例   \n",
       "21              江苏新增31例，累计确诊439例   \n",
       "22              黑龙江新增117例，累计234例   \n",
       "23                安徽新增68例，累计733例   \n",
       "24                  全国累计治愈出院超2千例   \n",
       "25      全国累计确诊新冠肺炎34546例，新增3399例   \n",
       "26  全国累计确诊新冠肺炎31774例，新增3399例新冠肺炎   \n",
       "27                广西新增11例，累计183例   \n",
       "28                  吉林新增4例，累计69例   \n",
       "29              河北新增24例，累计确诊195例   \n",
       "\n",
       "                                              summary infoSource  \\\n",
       "0   截至2月8日12时，全省累计报告新型冠状病毒感染的肺炎确诊病例1095例。8日当天0-12时...      广东卫健委   \n",
       "1   7日，国家邮政局召开部分快递企业专题电话会议指出，分阶段确定快递企业恢复生产目标，积极推进网...        人民网   \n",
       "2   今天，上海疫情防控工作发布会介绍：卫生防疫专家强调，目前可以确定的新冠肺炎传播途径主要为直接...       央视新闻   \n",
       "3   从湖北省新冠肺炎疫情防控指挥部了解到，今天省内企业自产N95口罩超过12万只，预计下周可实现...       人民日报   \n",
       "4   今天，安徽有12名新冠肺炎患者治愈出院。其中，合肥2例，铜陵2例，淮北1例，宿州1例，阜阳2...       央视新闻   \n",
       "5   新闻发言人介绍新型冠状病毒感染的肺炎统一称谓为“新型冠状病毒肺炎”，简称“新冠肺炎”，英文简...       人民日报   \n",
       "6   2020年2月7日22时至2月8日13时，辽宁省新增4例新型冠状病毒感染的肺炎确诊病例,其中...      辽宁卫健委   \n",
       "7   上海市卫健委今天通报：11例新冠肺炎病例，经定点医疗机构医护人员精心诊治和护理，专家组评估，...       央视新闻   \n",
       "8   2月7日0-24时，福建省报告新增新型冠状病毒肺炎确诊病例15例。报告新增新型冠状病毒肺炎疑...        人民网   \n",
       "9   2020年2月7日9时至8日10时，内蒙古自治区报告新型冠状病毒感染的肺炎新增确诊病例2例。...     内蒙古卫健委   \n",
       "10  2月7日0时至24时，北京市新增18例新型冠状病毒感染的肺炎病例。截至2月7日24时，北京市...        人民网   \n",
       "11  2020年2月7日12—24时，全省新型冠状病毒感染的肺炎新增确诊病例8例,新增治愈出院病例...        人民网   \n",
       "12  据国家卫健委数据统计，2月7日0—24时，全国湖北以外地区新增确诊病例558例，连续第4日呈...        人民网   \n",
       "13  2月8日，宁夏应对新型冠状病毒感染肺炎疫情工作指挥部发布最新全区新型冠状病毒感染的肺炎疫情通...        人民网   \n",
       "14  截至2月7日24时，广东省累计报告新型冠状病毒感染的肺炎确诊病例1075例。2月7日当天全省...       央视新闻   \n",
       "15  2020年2月7日0-24时，青海省报告新型冠状病毒感染的肺炎新增确诊病例0例，新增重症病例...        人民网   \n",
       "16  2020年2月7日0-24时，江西省报告新型冠状病毒感染的肺炎新增确诊病例37例，新增治愈出...       央视新闻   \n",
       "17  2020年2月7日0-24时，河南省新增新型冠状病毒感染的肺炎确诊病例67例,新增出院病例2...        人民网   \n",
       "18  2020年2月7日0-24时，浙江省报告新型冠状病毒感染的肺炎新增确诊病例42例，新增出院病...       央视新闻   \n",
       "19  2月7日0-24时，我省新型冠状病毒肺炎新增确诊病例19例，新增治愈出院病例13例，无新增死...        人民网   \n",
       "20  2020年2月7日0-24时，湖南省报告新型冠状病毒感染的肺炎新增确诊病例31例，新增重症病...       央视新闻   \n",
       "21  2020年2月7日0-24时，江苏省报告新型冠状病毒肺炎新增确诊病例31例。截至2月7日24...       央视新闻   \n",
       "22  2020年2月7日0-24时，黑龙江省报告新型冠状病毒感染的肺炎新增疑似病例117例，无症状...        人民网   \n",
       "23  2020年2月7日0-24时，安徽省报告新增确诊病例68例，新增疑似病例70例，新增治愈出院...        人民网   \n",
       "24  2月7日0—24时，31个省（自治区、直辖市）和新疆生产建设兵团报告新增治愈出院510例（湖...       央视新闻   \n",
       "25  2月7日0—24时，31个省（自治区、直辖市）和新疆生产建设兵团报告，新增确诊病例3399例...       央视新闻   \n",
       "26  2月7日0—24时，31个省（自治区、直辖市）和新疆生产建设兵团报告，新增确诊病例3399例...       央视新闻   \n",
       "27  2月7日0-24时，广西共新增新型冠状病毒感染的肺炎确诊病例11例。新增疑似病例123例，现...        人民网   \n",
       "28  2月7日0-24时，吉林省新增确诊病例4例，由疑似病例转为确诊病例1例，在密切接触者中主动开...        人民网   \n",
       "29  2020年2月7日0—24时，河北省报告新型冠状病毒感染的肺炎新增确诊病例24例。新增治愈出...       央视新闻   \n",
       "\n",
       "                                            sourceUrl     pubDate  \\\n",
       "0   http://wsjkw.gd.gov.cn/xxgzbdfk/yqtb/content/p...  2020-02-08   \n",
       "1       http://m.weibo.cn/2286908003/4469721326007093  2020-02-08   \n",
       "2       http://m.weibo.cn/2656274875/4469720700817749  2020-02-08   \n",
       "3       http://m.weibo.cn/2803301701/4469714216219398  2020-02-08   \n",
       "4       http://m.weibo.cn/2656274875/4469713495107536  2020-02-08   \n",
       "5       http://m.weibo.cn/2803301701/4469712253795751  2020-02-08   \n",
       "6   http://wsjk.ln.gov.cn/wst_zdzt/xxgzbd/yqtb/202...  2020-02-08   \n",
       "7       http://m.weibo.cn/2656274875/4469685842067584  2020-02-08   \n",
       "8       http://m.weibo.cn/2286908003/4469652887505637  2020-02-08   \n",
       "9   http://wjw.nmg.gov.cn/doc/2020/02/08/291119.shtml  2020-02-08   \n",
       "10      http://m.weibo.cn/2286908003/4469647812542639  2020-02-08   \n",
       "11      http://m.weibo.cn/2286908003/4469633911975612  2020-02-08   \n",
       "12      http://m.weibo.cn/2286908003/4469630049543788  2020-02-08   \n",
       "13      http://m.weibo.cn/2286908003/4469629193798369  2020-02-08   \n",
       "14      http://m.weibo.cn/2656274875/4469622834888584  2020-02-08   \n",
       "15      http://m.weibo.cn/2286908003/4469622763441202  2020-02-08   \n",
       "16      http://m.weibo.cn/2656274875/4469619902951551  2020-02-08   \n",
       "17      http://m.weibo.cn/2286908003/4469618531860175  2020-02-08   \n",
       "18      http://m.weibo.cn/2656274875/4469618426626283  2020-02-08   \n",
       "19      http://m.weibo.cn/2286908003/4469616015191183  2020-02-08   \n",
       "20      http://m.weibo.cn/2656274875/4469615893625003  2020-02-08   \n",
       "21      http://m.weibo.cn/2656274875/4469615188708391  2020-02-08   \n",
       "22      http://m.weibo.cn/2286908003/4469615009000259  2020-02-08   \n",
       "23      http://m.weibo.cn/2286908003/4469614513948374  2020-02-08   \n",
       "24      http://m.weibo.cn/2656274875/4469610981930220  2020-02-08   \n",
       "25      http://m.weibo.cn/2656274875/4469608121852346  2020-02-08   \n",
       "26      http://m.weibo.cn/2656274875/4469607526433947  2020-02-08   \n",
       "27      http://m.weibo.cn/2286908003/4469606099822822  2020-02-08   \n",
       "28      http://m.weibo.cn/2286908003/4469605475029507  2020-02-08   \n",
       "29      http://m.weibo.cn/2656274875/4469604535935240  2020-02-08   \n",
       "\n",
       "   provinceName provinceId             pubTime  \n",
       "0           广东省         44 2020-02-08 08:05:23  \n",
       "1            全国        100 2020-02-08 07:47:50  \n",
       "2            全国        100 2020-02-08 07:45:21  \n",
       "3           湖北省         42 2020-02-08 07:19:35  \n",
       "4           安徽省         34 2020-02-08 07:16:43  \n",
       "5            全国        100 2020-02-08 07:11:47  \n",
       "6           辽宁省         21 2020-02-08 06:41:00  \n",
       "7           上海市         31 2020-02-08 05:26:49  \n",
       "8           福建省         35 2020-02-08 03:15:53  \n",
       "9        内蒙古自治区         15 2020-02-08 03:00:13  \n",
       "10          北京市         11 2020-02-08 02:55:42  \n",
       "11          贵州省         52 2020-02-08 02:00:29  \n",
       "12          湖北省         42 2020-02-08 01:45:08  \n",
       "13      宁夏回族自治区         64 2020-02-08 01:41:43  \n",
       "14          广东省         44 2020-02-08 01:16:28  \n",
       "15          青海省         63 2020-02-08 01:16:11  \n",
       "16          江西省         36 2020-02-08 01:04:49  \n",
       "17          河南省         41 2020-02-08 00:59:22  \n",
       "18          浙江省         33 2020-02-08 00:58:57  \n",
       "19          四川省         51 2020-02-08 00:49:22  \n",
       "20          湖南省         43 2020-02-08 00:48:53  \n",
       "21          江苏省         32 2020-02-08 00:46:04  \n",
       "22         黑龙江省         23 2020-02-08 00:45:22  \n",
       "23          安徽省         34 2020-02-08 00:43:24  \n",
       "24           全国        100 2020-02-08 00:29:22  \n",
       "25           全国        100 2020-02-08 00:18:00  \n",
       "26           全国        100 2020-02-08 00:15:38  \n",
       "27      广西壮族自治区         45 2020-02-08 00:09:58  \n",
       "28          吉林省         22 2020-02-08 00:07:28  \n",
       "29          河北省         13 2020-02-08 00:03:44  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# today's news\n",
    "mask = (news['pubDate'] == datetime.date(year=2020, month=2, day=8))\n",
    "news[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scrapy shell -s USER_AGENT='Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36' \"https://www.baidu.com/s?ie=utf-8&cl=2&medium=0&rtt=1&bsst=1&rsv_dl=news_t_sk&tn=news&word=疫情+企业&rsv_sug3=1&rsv_sug2=0&inputT=14\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
